I've been meaning for a while to be more public about my investing, in order to share ideas with others and get feedback. Ideally I'd like to write up my thinking in detail, including describing what my target portfolio would be if I was more diligent about rebalancing (or didn't have to worry about tax planning). I haven't done either of those things. But, in order to not let the perfect be the enemy of the good, I'll just share very roughly what my current portfolio is.

My approximate current portfolio (note: I do not consider this to be optimal!):

• 40% TSLA
• 35% crypto -- XTZ, BTC, and ETH (and small amounts of LTC, XRP, and BCH)
• 25% startups -- Kinta AI, Coase, and General Biotics
• 4% diversified index funds
• 1% SQ (an exploratory investment -- there are some indications that I'd want to bet on them, but I want to do more research. Putting in a little bit of money forces me to start paying attention.)
• <1% FUV (another exploratory investment)
• -5% cash

Some notes:

• Once VIX comes down, I'll want to lever up a bit. Likely by increasing the allocation to index funds (and going more short cash).
• One major way this portfolio differs from the portfolio in my heart is that it has no exposure to Stripe. If it was easy to do, I would probably allocate something like 5-10% to Stripe.
• I have a high risk tolerance. I think both dispositionally, and because I buy 1) the argument from Lifecycle Investing that young(ish) people should be something like 2x leveraged and, 2) the argument that some EAs have made that people who plan to donate a lot should be closer to risk neutral than they otherwise would be. (Because your donations are a small fraction of the pool going to similar causes, so the utility in money is much closer to linear than for money you spend on yourself, which is probably something like logarithmic.)
• I am not very systematic. I follow my interests and go with my gut a lot. This has worked out surprisingly well. My crypto investment started with buying BTC at 25 cents in 2010, and my Tesla investment started at $35 in 2013. I've also invested in some startups that didn't work out, but my highest conviction gut bets (Tesla and bitcoin) have been the best performers, and have far more than made up for the misses.
• I would like to be more systematic. I think I would have done better up to now if I had been. Especially with tax planning.

After reading through some of the recent discussions on AI progress, I decided to sketch out my current take on where AI is and is going.

Hypotheses:

• The core smarts in our brains is a process that does self-supervised learning on sensory data.
• We share this smarts with animals.
• What distinguishes us from other animals is some kludgy stuff on top that enables us to:
  • 1) chain our data prediction intuitions into extended trains of thought via System 2 style reasoning
  • 2) share our thoughts with others via language
  • (and probably 1 and 2 co-evolved in a way that depended on each other)
• I say that the sensory data learning mechanism is the core smarts, rather than the trains-of-thought stuff, because the former is where the bulk of the computation takes place, and the latter is some relatively simple algorithms that channel that computation into useful work.
  • (Analogous to the kinds of algorithms Ought and others are building to coax GPT-3 into doing useful work.)
• Modern ML systems are doing basically the same thing as the predictive processing / System 1 / core smarts in our brains.
  • The details are different, but if you zoom out a bit, it's basically the same algorithm. And the natural and artificial systems are able to successfully model, compress, and predict data for basically the same reasons.
• AI systems will get closer to being able to match the full range of abilities of humans (and then exceed them) due to progress both on:
  • 1) improved intuition / data compression and prediction that comes from training bigger ML models for longer on more data, and
  • 2) better algorithms for directing those smarts into useful work.
• This means that basically no new fundamental insights are needed to get to AGI / TAI. It'll just be a bunch of iterative work to scale ML models, and productively direct their outputs.
• So the path from here looks pretty continuous, though there could be some jumpy parts, especially if some people are unusually clever (or brash) with the better algorithms (for making use of model outputs) part.

I'm curious if others agree with these claims. And if not, which parts seem most wrong?

This is my 1000th LessWrong comment. Hooray!

One of the most important things going on right now, that people aren't paying attention to: Kevin Buzzard is (with others) formalizing the entire undergraduate mathematics curriculum in Lean. (So that all the proofs will be formally verified.)

See one of his talks here: 

Some weird stuff happening with NKLA. That's the ticker for a startup called Nikola that did a reverse IPO last week (merging with the already-listed special-purpose company, VTIQ).

Nikola plans to sell various kinds of battery electric and hydrogen fuel cell trucks, with production scheduled to start in 2021.

When the reverse IPO was announced, the IPO price implied a valuation of NKLA at $3.3 billion. However, before the deal went through, the price of VTIQ rose from $10 in March to over $30 last week.

Then, after the combined company switched to the new ticker, NKLA, the price continued to rise, closing on Friday (June 5th) at $35, doubling on Monday to over $70 at close, and then continuing to rise to over $90 after hours, for a market cap over $30 billion, higher than the market cap of Ford.

The price has come down a bit today, and sits at $73 at the time I am writing this.

I have not investigated this company in detail. But some commentary from some amateur analysts whom I follow makes it sound to me like the hype has far outpaced the substance.

On Monday, I tried shorting at the open (via orders I'd placed the night before), but luckily for me, no shares were available to short (lucky since the price doubled that day). I tried again later in the day, and there were still no shares available.

It appears that the limited availability of shares to short has pushed traders into bidding up the prices of puts. If I'm reading the options chain right, it appears that a Jan 2022 synthetic long at a $50 strike (buying a $50 strike call and selling a $50 strike put) can be bought for roughly $0. Since the value of a synthetic long should be roughly equal to the price of the stock minus the strike, this implies a price of about $50 for the stock, in contrast to the $70+ price if you buy the stock directly.

That price discrepancy is so big that it seems like there's a significant chance I'm missing something. Can anybody explain why those options prices might actually make sense? Am I just doing the options math wrong? Is there some factor I'm not thinking of?

If I've heard him right, it sounds like Dan Selsam (NeuroSAT, IMO Grand Challenge) thinks ML systems will be able to solve IMO geometry problems (though not other kinds of problems) by the next IMO.

(See comments starting around 38:56.)

 

I'm looking for an old post where Eliezer makes the basic point that we should be able to do better than intellectual figures of the past, because we have the "unfair" advantage of knowing all the scientific results that have been discovered since then.

I think he cites in particular the heuristics and biases literature as something that thinkers wouldn't have known about 100 years ago.

I don't remember if this was the main point of the post it was in, or just an aside, but I'm pretty confident he made a point like this at least once, and in particular commented on how the advantage we have is "unfair" or something like that, so that we shouldn't feel at all sheepish about declaring old thinkers wrong.

Anybody know what post I'm thinking of?

GPT-X has a context (of some maximum size), and produces output based on that context. It's very flexible and can do many different tasks based on what's in the context.

To what extent is it reasonable to think of (part of) human cognition as being analogous, with working memory playing the role of context?

If I read the first half of a sentence of text, I can make some predictions about the rest, and in fact that prediction happens automatically when I'm prompted with the partial text (but can only happen when I keep the prompt in mind).

If I want to solve some math problem, I first load it into working memory (using my verbal loop and/or mental imagery), and then various avenues forward automatically present themselves.

Certainly, with human cognition there's some attentional control, and there's a feedback loop where you modify what's in working memory as you're going. So it's not as simple as just one-prompt, one-response, and then onto the next (unrelated) task. But it does seem to me like one part of human cognition (especially when you're trying to complete a task) is loading up semi-arbitrary data into working memory and then seeing what more opaque parts of your brain naturally spit out in response.

Does this seem like a reasonable analogy? In what ways is what our brains do with working memory the same as or different from what GPT-X does with context?

If GPT-3 is what you get when you do a massive amount of unsupervised learning on internet text, what do you get when you do a massive amount of unsupervised learning on video data from cars?

(In other words, can we expect anything interesting to come from Tesla's Dojo project, besides just better autopilot?)

Suppose you want to bet on interest rates rising -- would buying value stocks and shorting growth stocks be a good way to do it? (With the idea being that, if rates rise, future earnings will be discounted more and present earnings valued relatively more highly.)

And separately from whether long-value-short-growth would work, is there a more canonical or better way to bet on rates rising?

Just shorting bonds, perhaps? Is that the best you can do?

(Crossposted from Twitter)